The stemming rules for German are different from those for English, French,
Swedish, and so on. Applying the same stemming rules to different languages
will result in some words being stemmed correctly, some incorrectly, and some
not being stemmed at all. It may even result in words from different languages with different meanings
being stemmed to the same root word, conflating their meanings and producing
confusing search results for the user.

Applying multiple stemmers in turn to the same text is likely to result in
rubbish, as the next stemmer may try to stem an already stemmed word,
compounding the problem.

Stemmer per Script

The one exception to the only-one-stemmer rule occurs when each language
is written in a different script. For instance, in Israel it is quite
possible that a single document may contain Hebrew, Arabic, Russian (Cyrillic),
and English:

אזהרה - Предупреждение - تحذير - Warning

Each language uses a different script, so the stemmer for one language will not
interfere with another, allowing multiple stemmers to be applied to the same
text.

In What Is Relevance?, we explained that the more frequently a term appears
in a collection of documents, the less weight that term has. For accurate
relevance calculations, you need accurate term-frequency statistics.

A short snippet of German appearing in predominantly English text would give
more weight to the German words, given that they are relatively uncommon. But
mix those with documents that are predominantly German, and the short German
snippets now have much less weight.

It is not sufficient just to think about your documents, though. You also need
to think about how your users will query those documents. Often you will be able
to identify the main language of the user either from the language of that user’s chosen
interface (for example, mysite.de versus mysite.fr) or from the
accept-language
HTTP header from the user’s browser.

User searches also come in three main varieties:

Users search for words in their main language.

Users search for words in a different language, but expect results in
their main language.

Users search for words in a different language, and expect results in
that language (for example, a bilingual person, or a foreign visitor in a web cafe).

Depending on the type of data that you are searching, it may be appropriate to
return results in a single language (for example, a user searching for products on
the Spanish version of the website) or to combine results in the identified
main language of the user with results from other languages.

Usually, it makes sense to give preference to the user’s language. An English-speaking
user searching the Web for “deja vu” would probably prefer to see
the English Wikipedia page rather than the French Wikipedia page.

You may already know the language of your documents. Perhaps your documents
are created within your organization and translated into a list of predefined
languages. Human pre-identification is probably the most reliable method of
classifying language correctly.

Perhaps, though, your documents come from an external source without any
language classification, or possibly with incorrect classification. In these
cases, you need to use a heuristic to identify the predominant language.
Fortunately, libraries are available in several languages to help with this problem.

Identifying the language of the user’s search request is not quite as simple.
The CLD is designed for text that is at least 200 characters in length.
Shorter amounts of text, such as search keywords, produce much less accurate
results. In these cases, it may be preferable to take simple heuristics into
account such as the country of origin, the user’s selected language, and the
HTTP accept-language headers.